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Similarity computation using semantic networks created from web-harvested data

Published online by Cambridge University Press:  26 July 2013

ELIAS IOSIF  and
ALEXANDROS POTAMIANOS
ELIAS IOSIF
Affiliation:
Department of Electronic and Computer Engineering, Technical University of CreteChania 73100, Greece email: iosife@telecom.tuc.gr, potam@telecom.tuc.gr
ALEXANDROS POTAMIANOS
Affiliation:
Department of Electronic and Computer Engineering, Technical University of CreteChania 73100, Greece email: iosife@telecom.tuc.gr, potam@telecom.tuc.gr
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Abstract

We investigate language-agnostic algorithms for the construction of unsupervised distributional semantic models using web-harvested corpora. Specifically, a corpus is created from web document snippets, and the relevant semantic similarity statistics are encoded in a semantic network. We propose the notion of semantic neighborhoods that are defined using co-occurrence or context similarity features. Three neighborhood-based similarity metrics are proposed, motivated by the hypotheses of attributional and maximum sense similarity. The proposed metrics are evaluated against human similarity ratings achieving state-of-the-art results.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Issue 1 , January 2015 , pp. 49 - 79
Copyright
Copyright © Cambridge University Press 2013 

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